An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

Sources and mitigation of methane emissions by sectors: A critical review

The environmental community rightly recognizes global warming as one of the gravest threats to the planet. Methane (CH₄), one of the greenhouse gases causing global warming, is emitted from a variety of sources and its concentration in atmosphere has increased dramatically over the last few centuries. Therefore, the increasing concentrations of methane are of special concern because of its effects on climate and atmospheric chemistry. Anthropogenic sources of methane can be collected under the titles of agriculture, energy, waste and industry on the basis of sectors. This paper aims at examining the past trends in emissions, the sources and mitigation strategies of the methane. As a result of the study, it is determined that the agricultural sector is the biggest source of methane emissions among the sectors. The energy, waste and industry follow the agricultural sources respectively.     

Investigations of ductile damage during the process chains of toothed functional components manufactured by sheet-bulk metal forming

Sheet-bulk metal forming processes combine conventional sheet forming processes with bulk forming of sheet semi-finished parts. In these processes the sheets undergo complex forming histories. Due to in- and out-of-plane material flow and large accumulated plastic strains, the conventional failure prediction methods for sheet metal forming such as forming limit curve fall short. As a remedy, damage models can be applied to model damage evolution during those processes. In this study, damage evolution during the production of two different toothed components from DC04 steel is investigated. In both setups, a deep drawn cup is upset to form a circumferential gearing. However, the two final products have different dimensions and forming histories. Due to combined deep drawing and upsetting processes, the material flow on the cup walls is three-dimensional and non-proportional. In this study, the numerical and experimental investigations for those parts are presented and compared. Damage evolution in the process chains is simulated with a Lemaitre damage criterion. Microstructural analysis by scanning electron microscopy is performed in the regions with high mechanical loading. It is observed that the evolution of voids in terms of void volume fraction is strongly dependent on the deformation path. The comparison of simulation results with microstructural data shows that the void volume fraction decreases in the upsetting stage after an initial increase in the drawing stage. Moreover, the concurrent numerical and microstructural analysis provides evidence that the void volume fraction decreases during compression in sheet-bulk metal forming.     

Magnetic Properties of Zn1?x Ni x O (0.25≤x≤ 0.50) Prepared by Solid-State Reactions

In this study the origin of ferromagnetism in ZnO-based bulk systems has been investigated using Ni-doped ZnO samples, Zn_1−x Ni _x O with 0.25≤x≤0.50, prepared by solid-state reactions. The structural characterizations indicated that the Ni²⁺ ions almost uniformly distributed in all the samples, and the samples have hexagonal wurtzite structure; however, when x is increased toward 0.50, a new NiO phase is formed. A ferromagnetism (FM) has been observed for all the samples at and below the room temperature. In other words, the room temperature results of (M–H) curves show that the FM observed is intrinsic for all the Ni-doped ZnO samples. However, the saturated magnetizations decrease gradually with increasing Ni concentration. This indicates that, in addition to FM, the excessive doping of Ni in ZnO also causes an antiferromagnetic (AFM) contribution which increases with increasing Ni amount. This result is also supported by the magnetization against temperature measurements. Furthermore, the trend of the ac-susceptibility (χ) versus temperature curves, measured under an ac-magnetic field of 100 Oe, also support our conclusion about the antiferromagnetic contribution to ferromagnetism in our samples.     

Reflective light modulator based on epsilon-GaSe crystal

We report the results of investigating a low-voltage, polarization-insensitive, reflective-type modulator based on an epsilon-GaSe crystal and operated at the 1.960-eV line of a He-Ne laser. We demonstrate that the modulation in an Al-epsilon-GaSe-Cu device results mainly from the Franz-Keldysh effect. Relatively high speed and low operating voltage could make these modulators with Schottky-barrier contacts attractive devices in the red range of the spectrum.     

Neural Models for the Resonant Frequency of Electrically Thin and Thick Circular Microstrip Antennas and the Characteristic Parameters of Asymmetric Coplanar Waveguides Backed with a Conductor

Neural models for computing the resonant frequency of electrically thin and thick circular microstrip antennas, based on the multilayered perceptrons and the radial basis function networks, are presented. Five learning algorithms, delta-bar-delta, extended delta-bar-delta, quick-propagation, directed random search and genetic algorithms, are used to train the multilayered perceptrons. The radial basis function network is trained according to its learning strategy. The resonant frequency results of neural models are in very good agreement with the experimental results available in the literature. In this paper, the characteristic impedance and the effective permittivity of the asymmetric coplanar waveguide backed with a conductor are also computed by using only one neural model trained by the backpropagation with momentum and the extended delta-bar-delta algorithms. When the performances of neural models are compared with each other, the best results for test are obtained from the multilayered perceptrons trained by the extended delta-bar-delta algorithm.     

Bonding performance of universal adhesives to er,cr:YSGG laser‐irradiated enamel

Universal adhesives have been recently introduced for use as self‐etch or etch‐and‐rinse adhesives depending on the dental substrate and clinical condition. However, their bonding effectiveness to laser‐irradiated enamel is still not well‐known. Thus, the aim of this study was to compare the shear bond strength (SBS) of universal adhesives (Single Bond Universal; Nova Compo‐B Plus) applied to Er,Cr:YSGG laser‐irradiated enamel with SBS of the same adhesives applied in self‐etch and acid‐etching modes, respectively. Crown segments of sixty bovine incisors were embedded into standardized acrylic blocks. Flattened enamel surfaces were prepared. Specimens were divided into six groups according to universal adhesives and application modes randomly (n = 10), as follows: Single Bond Universal/acid‐etching mode; Nova Compo‐B Plus/acid‐etching mode; Single Bond Universal/self‐etching mode; Nova Compo‐B Plus/self‐etching mode; and Single Bond Universal/Er,Cr:YSGG Laser‐etching mode; Nova Compo‐B Plus/Er,Cr:YSGG Laser‐etching mode. After surface treatments, universal adhesives were applied onto surfaces. SBS was determined after storage in water for 24 h using a universal testing machine with a crosshead speed of 0.5 mm min^?1. Failure modes were evaluated using a stereomicroscope. Data was analyzed using two‐way of analyses of variances (ANOVA) (p = 0.05). Two‐way ANOVA revealed that adhesive had no effect on SBS (p = 0.88), but application mode significantly influenced SBS (p = 0.00). Acid‐etching significantly increased SBS, whereas there are no significant differences between self‐etch mode and laser‐etching for both adhesives. The bond strength of universal adhesives may depend on application mode. Acid etching may significantly increase bond strength, while laser etching may provide similar bond strength when compared to self‐etch mode.     

Comprehensive performance characterization of conductive fabric made of reduced graphene oxide (rGO)

In this study, the production and comprehensive characterization of graphene based electrically conductive textile products, which will allow lower processing costs, such as, low temperature and short process times, and known textile production processes without losing their flexibility, lightness, permeability, and washability characteristics of textile products. Reduced graphene oxide (rGO) was applied with a single step process by impregnation to woven fabrics and various properties of fabrics related to performance and comfort were analyzed besides electrical conductivity. The results obtained in the study have shown that it is possible to produce flexible and soft conductive textiles with low resistance values below 100 kΩ, resistant to mechanical deformations, such as, bending, rubbing, and stretching (cyclic test). In addition, the findings indicated that the fabrics applied rGO with the one-step production process will provide significant advantages to the electronic textile market in terms of wearability, cost-effectiveness, and applicable processes.